Model-Based Decisions in Sepsis

描述（由申请人提供）：对脓毒症等急性炎症性疾病进行免疫调节干预的大型随机临床试验的记录令人沮丧。正如FDA关键路径文件和NIH路线图倡议所提出的那样，宿主-病原体相互作用的生物学复杂性以及成功治疗对医疗保健系统和社会的潜在巨大影响使败血症等疾病成为基于模型的治疗方法的理想试验台。然而，机体水平的炎症计算模型缺乏。然而，更根本的是，缺乏人类数据集来验证这些模型。这样的数据集的收集成本非常高，而且在缺乏证明有效性的模型的情况下，仅用于测试基于模型的干预措施是极不可能获得的。美国国立卫生研究院（nih）资助的早期脓毒症休克治疗方案（ProCESS）研究目前正在一项1350名患者的前瞻性随机试验中研究早期复苏对严重脓毒症患者的影响，并将产生一个具有粒度的数据集，不仅有助于了解脓毒症的过程，而且有助于了解生理目标导向治疗方案的生物学后果。本提案中概述的项目总体目标是通过先进的数学和计算方法，利用ProCESS研究的数据验证人类败血症的计算模型。我们组建了一个由建模师和临床医生组成的跨学科小组，他们在开发、校准和测试不同粒度水平的急性炎症，特别是败血症的计算机模型方面有着良好的合作记录。我们认为，在大型临床相关队列中验证计算机模型对于计算模型的合法化至关重要，计算模型作为一种技术，将被证明是设计更智能的随机介入性试验的关键，特别是个性化治疗。一方面利用ProCESS试验的数据和初步分析，另一方面利用现有的广泛的跨学科努力来扩展现有的急性炎症反应计算模型，也将为获得导致器官衰竭和死亡、系统恢复和意外衰竭的过程的机制理解提供前所未有的机会。

DESCRIPTION (provided by applicant): Large randomized clinical trials of immunomodulatory interventions for acute inflammatory diseases such as sepsis have had a dismal track record. The biological complexity of the host-pathogen interaction and the potential large impact of a successful treatment on the health care system and society position diseases such as sepsis as ideal test beds for model-based therapeutic approaches, as proposed in the FDA critical path document and the NIH roadmap initiative. Yet, there is a paucity of organism-level computational models of inflammation. More fundamental however, is the lack of human data sets where such models could be validated. Such a data set would be extraordinarily expensive to assemble and is highly unlikely to be acquired merely for testing model-based interventions in the absence of models with demonstrated validity. The NIH-funded Protocolized Care for Early Septic Shock (ProCESS) study is currently examining the impact of early resuscitation in victims of severe sepsis in a 1350 patient prospective randomized trial and will produce a data set with a granularity that will not only help to understand the processes involved in sepsis, but also the biological consequences of a physiologic goal-directed treatment protocol. The overarching goal of the program outlined in this proposal is to validate computational models of human sepsis using data from the ProCESS study through advanced mathematical and computational methods. We have assembled a transdisciplinary group of modelers and clinicians with an eloquent track record of successful collaboration on developing, calibrating and testing in silico models of acute inflammation, and of sepsis in particular, of different levels of granularity. We believe that validation of in silico models in a large clinically relevant cohort is absolutely cruial to the legitimization of computational modeling as a technology that will prove pivotal to the design of smarter randomized interventional trials in general, and of personalized therapies in particular. Leveraging data and preliminary analyses from the ProCESS trial on the one hand and an extensive existing transdisciplinary effort at expanding existing computational models of the acute inflammatory response on the other will also provide an unprecedented opportunity to gain mechanistic understanding of the processes leading to organ failure and death, systemic recovery and unexpected failure.